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A deep neural network Model for Upper Limb Swing Pattern to Control an Active Bionic Leg

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Instrumentation 2021年第1期8卷 51-60页

作者： Thisara PATHIRANA Hiroshan GUNAWARDANE Nimali T MEDAGEDARA Department of Mechanical Engineering Faculty of Engineering TechnologyThe Open University of Sri LankaNugegodaSri Lanka Department of Mechanical Engineering Faculty of Applied ScienceThe University of British ColumbiaVancouverCanada

Leg amputations are common in accidents and *** present active bionic legs use Electromyography(EMG)signals in lower limbs(just before the location of the amputation)to generate active control *** active control with EMGs greatly limits the potential of using these bionic legs because most accidents and diseases cause severe damages to tissues/muscles which originates EMG *** an alternative,the present research attempted to use an upper limb swing pattern to control an active bionic leg.A deep neural network(DNN)model is implemented to recognize the patterns in upper limb swing,and it is used to translate these signals into active control input of a bionic *** proposed approach can generate a full gait cycle within 1082 milliseconds,and it is comparable to the normal(a person without any disability)1070 milliseconds gait cycle.

关键词： Active Bionic Leg deep neural network Human Gait Cycle Time

Leap Motion Hand Gesture Recognition Based on deep neural network

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Leap Motion Hand Gesture Recognition Based on Deep Neural Ne...

第32届中国控制与决策会议

作者： Qinglian Yang Weikang Ding Xingwen Zhou Dongdong Zhao Shi Yan School of Information Science and Engineering Lanzhou University

This paper proposes a new gesture recognition system based on deep neural network（DNN） and Leap *** palm model is reconstructed to obtain the feature data,and then the feature data of all experimenters are obtained by the Leap Motion *** data are finally put into the DNN model for training to implement the recognition of specific hand gesture after being *** testing with 30000 gesture frames of five volunteers,it is found that the recognition accuracy of the proposed scheme can reach 98%,which indicates that the scheme can complete the specific gesture recognition with a high average recognition rate.

关键词： Leap Motion sensor gesture recognition deep neural network

A Weld Defect Detection Method Based on Triplet deep neural network

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A Weld Defect Detection Method Based on Triplet Deep Neural ...

第32届中国控制与决策会议

作者： Xiaoyuan Liu Jinhai Liu Fuming Qu Hongfei Zhu Danyu Lu School of Information Science and Engineering Northeastern University

In industrial fields, Nondestructive Testing（NDT） has become an important method to test the quality of welds. For the low-contrast pipe weld defect x-ray image, the traditional detection method has low precision. In this paper, an automatic detection method for weld defects based on a triplet deep neural network is proposed. First, the original X-ray image is changed into a relief image, so that the feature of the defects is more obvious. Second, the feature vector is obtained by mapping the relief image through the triplet deep neural network. The deep neural network based on triplet makes the similar defect feature vectors are closer, and the distances of different defect feature vectors are farther. It is first time that the deep neural network based on triplet was used to detect the weld defect images. Finally, the weld defect was detected by Support Vector Machine（SVM） classifier. It is shown that the proposed detection method of weld defects has better performance than the conventional methods.

关键词： defect detection x-ray triplet loss deep neural network

Hybrid deep neural network based on SDAE and GRUNN

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Hybrid Deep Neural Network based on SDAE and GRUNN

第三十九届中国控制会议

作者： Yingyong Zou Jun Yu Jiangen Tang Yongde Zhang School of Mechanical and Power Engineering Harbin University of Science and Technology College of Mechanical and Vehicular Engineering Changchun University Key Laboratory of Advanced Manufacturing and Intelligent Technology Harbin University of Science and Technology School of Automation Harbin University of Science and Technology

Stacked autoencoder（SAE） is hard to achieve satisfactory performance,when input data are complex and ***,the identification performance of recurrent neural network（RNN） may decrease rapidly under noisy *** order to deal with these problems,a novel hybrid deep neural network（DNN） based on stacked denoising autoencoder（SDAE） and gated recurrent unit neural network（GRUNN） is ***,the structure of the presented hybrid DNN is *** hybrid DNN contains a SDAE,a GRUNN,and a softmax ***,the training algorithm based on action discovery（AD） is proposed to train the presented hybrid *** experimental studies indicate the presented hybrid DNN processes strong anti-noise ability and adaptability to time-varying signals.

关键词： deep neural network stacked denoising autoencoder gated recurrent unit neural network denoising ability action discovery

A Jamming Recognition Algorithm Based on deep neural network in Satellite Navigation System

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A Jamming Recognition Algorithm Based on Deep Neural Network...

第十一届中国卫星导航年会

作者： Hao Xu Yufan Cheng Jindi Liang Pengyu Wang National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China

Jamming recognition technology plays an important role in satellite navigation anti-jamming systems. Accurate and reliable recognition of jamming types is a necessary premise for adopting targeted ant

关键词： satellite navigation deep neural network jamming classifier jamming recognition compound jamming

Solving Schrodinger Equation with Soft Constrained Monotonic neural network

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原子核物理评论 2024年第1期41卷 379-384页

作者： LIU Xuan LI Hanlin PU Kaifang PANG Longgang College of Science Wuhan University of Science and TechnologyWuhan 430065China Key Laboratory of Quark and Lepton Physics(MOE)and Institute of Particle Physics Central China Normal UniversityWuhan 430079China

Artificial neural network(ANN)has become a powerful tool in the field of scientific research with its powerful information encapsulation ability and convenient variational optimization *** particular,there have been many recent advances in computational physics to solve variational *** neural network(DNN)is used to represent the wave function to solve quantum many-body problems using variational *** this work we used a new Physics-Informed neural network(PINN)to represent the Cumulative Distribution Function(CDF)of some classical problems in quantum mechanics and to obtain their ground state wave function and ground state energy through the *** benchmarking against the exact solution,the error of the results can be controlled at a very low *** new network architecture and optimization method can provide a new choice for solving quantum many-body problems.

关键词： deep neural network variational problem Cumulative distribution function ground state wave function

Surface wave inversion with unknown number of soil layers based on a hybrid learning procedure of deep learning and genetic algorithm

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Earthquake Engineering and Engineering Vibration 2024年第2期23卷 345-358页

作者： Zan Zhou Thomas Man-Hoi Lok Wan-Huan Zhou Department of Civil and Environmental Engineering Faculty of Science and TechnologyUniversity of MacaoMacaoChina

Surface wave inversion is a key step in the application of surface waves to soil velocity ***,a common practice for the process of inversion is that the number of soil layers is assumed to be known before using heuristic search algorithms to compute the shear wave velocity profile or the number of soil layers is considered as an optimization ***,an improper selection of the number of layers may lead to an incorrect shear wave velocity *** this study,a deep learning and genetic algorithm hybrid learning procedure is proposed to perform the surface wave inversion without the need to assume the number of soil ***,a deep neural network is adapted to learn from a large number of synthetic dispersion curves for inferring the layer ***,the shear-wave velocity profile is determined by a genetic algorithm with the known layer *** applying this procedure to both simulated and real-world cases,the results indicate that the proposed method is reliable and efficient for surface wave inversion.

关键词： surface wave inversion analysis shear-wave velocity profile deep neural network genetic algorithm

DCVAE-adv:A Universal Adversarial Example Generation Method for White and Black Box Attacks

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Tsinghua Science and Technology 2024年第2期29卷 430-446页

作者： Lei Xu Junhai Zhai College of Mathematics and Information Science Hebei UniversityBaoding 071002China

deep neural network(DNN)has strong representation learning ability,but it is vulnerable and easy to be fooled by adversarial *** order to handle the vulnerability of DNN,many methods have been *** general idea of existing methods is to reduce the chance of DNN models being fooled by observing some designed adversarial examples,which are generated by adding perturbations to the original *** this paper,we propose a novel adversarial example generation method,called *** from the existing methods,DCVAE-adv constructs adversarial examples by mixing both explicit and implicit perturbations without using original ***,the proposed method can be applied to both white box and black box *** addition,in the inference stage,the adversarial examples can be generated without loading the original images into memory,which greatly reduces the memory *** compared DCVAE-adv with three most advanced adversarial attack algorithms:FGSM,AdvGAN,and AdvGAN++.The experimental results demonstrate that DCVAE-adv is superior to these state-of-the-art methods in terms of attack success rate and transfer ability for targeted *** code is available at https://***/xzforeverlove/DCVAE-adv.

关键词： deep neural network adversarial examples white box attack black box attack robustness

An Intelligent deep neural Sentiment Classification network

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Intelligent Automation & Soft Computing 2023年第5期36卷 1733-1744页

作者： Umamaheswari Ramalingam Senthil Kumar Murugesan Karthikeyan Lakshmanan Chidhambararajan Balasubramaniyan Department of Electronics and Instrumentation Engineering SRM Valliammai Engineering CollegeKattankulathurTamilnaduIndia Department of Computer Science and Engineering SRM Valliammai Engineering CollegeKattankulathurTamilnaduIndia Department of Electronics and Communications Engineering SRM Valliammai Engineering CollegeKattankulathurTamilnaduIndia

A deep neural Sentiment Classiﬁcation network(DNSCN)is devel-oped in this work to classify the Twitter data *** attempts to extract the negative and positive sentiments in the Twitter *** main goal of the system is toﬁnd the sentiment behavior of tweets with minimum ambiguity.A well-deﬁned neural network extracts deep features from the tweets *** extracting features deeper and deeper,the text in each tweet is represented by Bag-of-Words(BoW)and Word Embeddings(WE)*** effectiveness of DNSCN architecture is analyzed using Twitter-Sanders-Apple2(TSA2),Twit-ter-Sanders-Apple3(TSA3),and Twitter-DataSet(TDS).TSA2 and TDS consist of positive and negative tweets,whereas TSA3 has neutral tweets ***,the proposed DNSCN acts as a binary classiﬁer for TSA2 and TDS databases and a multiclass classiﬁer for *** performances of DNSCN architecture are evaluated by F1 score,precision,and recall rates using 5-fold and 10-fold *** show that the DNSCN-WE model provides more accuracy than the DNSCN-BoW model for representing the tweets in the feature *** F1 score of the DNSCN-BW based system on the TSA2 database is 0.98(binary classiﬁcation)and 0.97(three-class classiﬁcation)for the TSA3 *** system provides better a F1 score of 0.99 for the TDS database.

关键词： deep neural network word embeddings bag-of-words sentiment analysis text classiﬁcation